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Incidental durotomy: predictive risk model and external validation of natural language process identification

Jeff Ehresman¹, Zach Pennington¹, Aditya V Karhade²

¹1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland; and.

Journal of Neurosurgery. Spine

|May 2, 2020

Summary

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This study identified predictors of incidental durotomy in lumbar spine surgery, developing a risk calculator. Natural language processing also accurately identified dural tears, aiding complication prevention.

Area of Science:

Neurosurgery
Spine Surgery
Surgical Complications

Background:

Incidental durotomy, a common complication in elective lumbar spine surgery, occurs in up to 11% of cases.
Previous research suggests patient age, body mass index, and prior surgery history are risk factors.
A validated tool for quantifying incidental durotomy risk is currently lacking.

Purpose of the Study:

To identify independent predictors of incidental durotomy in elective lumbar spine surgery.
To develop and present a novel predictive calculator for incidental durotomy risk.
To externally validate a natural language processing (NLP) method for identifying incidental durotomies.

Main Methods:

Retrospective review of 1279 patients undergoing elective lumbar spine surgery for degenerative pathologies.

Keywords:

incidental durotomy intraoperative complication lumbar spine surgery natural language processing

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Data collection included surgical details, demographics, and comorbidities; incidental durotomy identified manually and via NLP.

Multivariable logistic regression and bootstrapping were used to develop and validate the predictive model and calculator.

Main Results:

Incidental durotomy occurred in 8.4% (108/1279) of patients.
Independent predictors identified: increased surgical duration, older age, revision surgery, and cases starting after 4 pm (AUC 0.73).
The NLP method demonstrated excellent discrimination for identifying incidental durotomies (AUC 0.97).

Conclusions:

Increased surgical duration, older age, prior surgery, and late start times are significant predictors of incidental durotomy.
A novel clinical calculator for durotomy risk has been developed for prospective use in patient counseling.
Validated NLP tools can effectively identify incidental durotomies from operative notes, aiding research and complication prevention.